class_docstr: :param trace_dist: a TracePosterior instance representing a Monte Carlo posterior
  
  Marginal histogram distribution.
  Turns a TracePosterior object into a Distribution
  over the return values of the TracePosterior's model.
code: def _dist_and_values(self):
      # XXX currently this whole object is very inefficient
      values_map, logits = (
          collections.OrderedDict(),
          collections.OrderedDict(),
      )
      for tr, logit in zip(
          self.trace_dist.exec_traces, self.trace_dist.log_weights
      ):
          if isinstance(self.sites, str):
              value = tr.nodes[self.sites]["value"]
          else:
              value = {
                  site: tr.nodes[site]["value"] for site in self.sites
              }
          if not torch.is_tensor(logit):
              logit = torch.tensor(logit)
  
          if torch.is_tensor(value):
              value_hash = hash(
                  value.cpu().contiguous().numpy().tobytes()
              )
          elif isinstance(value, dict):
              value_hash = hash(self._dict_to_tuple(value))
          else:
              value_hash = hash(value)
          if value_hash in logits:
              # Value has already been seen.
              logits[value_hash] = logsumexp(
                  torch.stack([logits[value_hash], logit]), dim=-1
              )
          else:
              logits[value_hash] = logit
              values_map[value_hash] = value
  
      logits = torch.stack(list(logits.values())).contiguous().view(-1)
      logits = logits - logsumexp(logits, dim=-1)
      d = dist.Categorical(logits=logits)
      return d, values_map
  
imports: import collections
  import functools
  import queue
  import torch
  import pyro.distributions
  import pyro.poutine
  from pyro.distributions.util import logsumexp
  from pyro.infer.abstract_infer import TracePosterior
  from pyro.poutine.runtime import NonlocalExit
label_desc: Use image node_id 2 for calling the HashingMarginal obj's underlying member method code with example usage: obj._dist_and_values() and returns: d,  values_map